README.md

---
license: bigcode-openrail-m
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: bigcode/starcoder2-3b
model-index:
- name: finetunedPHP_starcoder2
  results: []
datasets:
- bigcode/the-stack-smol
language:
- en
---

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# finetunedPHP_starcoder2

This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) on [bigcode/the-stack-smol](https://huggingface.co/datasets/bigcode/the-stack-smol).

## Model description

The `finetunedPHP_starcoder2` model is based on the `starcoder2-3b` architecture, fine-tuned specifically on PHP code from `the-stack-smol` dataset. It is intended for code generation tasks related to PHP programming.

## Intended uses & limitations

The `finetunedPHP_starcoder2` model is suitable for generating PHP code snippets for various purposes, including code completion, syntax suggestions, and code generation tasks. However, it may have limitations in generating complex or domain-specific code, and users should verify the generated code for correctness and security.

## Training and evaluation data

The model was trained on a dataset consisting of PHP code samples collected from `the-stack-smol` dataset. The training data included code snippets from PHP repositories, forums, and online tutorials.

## Training procedure

**1. Data and Model Preparation:**

- Load the PHP dataset from my repository `bigcode/the-stack-smol`.
- Extract the relevant PHP data `data/php` samples for training.
- Utilize the `starcoder2-3b` model pre-trained on a diverse range of programming languages, including PHP, from the Hugging Face Hub.
- Ensure the model is configured with '4-bit' quantization for efficient computation.

**2. Data Processing:**

- Tokenize the PHP code snippets using the model's tokenizer.
- Clean the code by removing comments and normalizing indentation.
- Prepare input examples suitable for the model, considering its architecture and objectives.

**3. Training Configuration:**

- Initialize a Trainer object for fine-tuning, leveraging the Transformers library.
- Define training parameters, including:
    - Learning rate, optimizer, and scheduler settings.
    - Gradient accumulation steps to balance memory usage.
    - Loss function, typically cross-entropy for language modeling.
    - Metrics for evaluating model performance.
    - Specify GPU utilization for accelerated training.
    - Handle potential distributed training with multiple processes.

**4. Model Training:**

- Commence training for a specified number of steps.
- Iterate through batches of preprocessed PHP code examples.
- Feed examples into the model and compute predictions.
- Calculate loss based on predicted and actual outcomes.
- Update model weights by backpropagating gradients through the network.

**5. Evaluation (Optional):**

- Periodically assess the model's performance on a validation set.
- Measure key metrics such as code completion accuracy or perplexity.
- Monitor training progress to fine-tune hyperparameters if necessary.
- Use wandb metric monitoring for live monitoring.

**6. Save the Fine-tuned Model:**

- Store the optimized model weights and configuration in the designated `output_dir`.

**7. Model Sharing (Optional):**

- Optionally, create a model card documenting the fine-tuning process and model specifications.
- Share the finetunedPHP_starcoder2 model on the Hugging Face Hub for broader accessibility and collaboration.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

Training results and performance metrics are present in the repo.

### Framework versions

- PEFT 0.8.2
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2